1. Field of the Invention
The present invention relates to a digital image processing technique, and more particularly to methods and apparatuses for processing medical images and for performing thickness compensation in medical images for anatomical objects.
2. Description of the Related Art
Mammograms are routinely acquired in hospitals, to screen for breast cancer or other breast abnormalities. A radiologist may analyze the mammograms using image attributes such as breast appearance, color, etc., to identify breast tumors, to observe differences between breasts images, to establish a baseline for the mammographic parenchyma of the patient, etc.
Typical/conventional processing techniques for mammographic images assume that a breast is compressed into an object of constant thickness in a mammography machine. With this assumption, grayscale values in a mammographic image are considered to be directly correlated to the structure of breast tissue.
The assumption that the breast is compressed into an object of constant thickness in a mammography machine is not correct, however. For example, the thickness of the breast gradually decreases in peripheral areas of a compressed breast, such as in areas found at the skin line. In addition, the plates compressing the breast are not parallel in some mammogram scanners. For these and other reasons, a large portion of the compressed breast area does not actually satisfy the assumption of constant thickness. Thickness variation of a compressed breast leads to differences in exposure in the mammography scanner. Hence, the resulting mammography image does not image a constant thickness breast. Breast thickness variation associated with the mammography image and complex color variation in the mammography image degrade performance of subsequent CAD processes applied to the image.
Disclosed embodiments of this application address these and other issues by performing thickness compensation for anatomical objects, such as breast images compressed in a mammography machine. A layer map is generated for a breast and layer thickness within each layer is estimated. Embodiments of this application use a model based thickness estimate method, followed by relaxed global refinement. When used for breast thickness compensation, the model and outlier detection take into account properties of breast tissue and of breast compression. A strong model assumption is used for initial breast thickness compensation, and weak assumptions are used to refine thickness estimation. The refinement step performed after the model based thickness estimation process takes into account characteristics of breast compression and imposes fewer constraints. In a preferred embodiment, the compensating step estimates layer thickness for the breast image using a semi-circle thickness model, and uses a logistic model to initialize a semi-circle estimate. The methods and apparatuses of the present invention may be applied to images of breasts and to images of other anatomical objects besides breasts.